# coding=utf-8 ''' Created on 2017年5月16日 @vsersion:python3.6 @author: liuyuqi ''' # 导入开发模块 import requests from bs4 import BeautifulSoup # 定义空列表,用于创建所有的爬虫链接 urls = [] # 指定爬虫所需的上海各个区域名称 citys1 = ['pudongxinqu', 'minhang', 'baoshan', 'xuhui', 'putuo', 'yangpu', 'changning', 'songjiang', 'jiading', 'huangpu', 'jinan', 'zhabei', 'hongkou', 'qingpu', 'fengxian', 'jinshan', 'chongming'] citys = ['pudongxinqu'] data = {"user": "user", "password": "pass"} headers = {"Accept": "text/html,application/xhtml+xml,application/xml;", "Accept-Encoding": "gzip", "Accept-Language": "zh-CN,zh;q=0.8", "Referer": "http://www.example.com/", "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.90 Safari/537.36" } # 基于for循环,构造完整的爬虫链接 for i in citys: url = 'http://sh.lianjia.com/ershoufang/%s/' % i res = requests.get(url, headers=headers) res = res.text.encode(res.encoding).decode('utf-8') # 需要转码,否则会有问题 soup = BeautifulSoup(res, 'html.parser') # 使用bs4模块,对响应的链接源代码进行html解析 page = soup.findAll('div', {'class': 'c-pagination'}) # 使用finalAll方法,获取指定标签和属性下的内容 pages = [i.strip() for i in page[0].text.split('\n')] # 抓取出每个区域的二手房链接中所有的页数 # print(pages) if len(pages) > 3: total_pages = int(pages[-3]) else: total_pages = int(pages[-2]) for j in list(range(1, total_pages + 1)): # 拼接所有需要爬虫的链接 urls.append('http://sh.lianjia.com/ershoufang/%s/d%s' % (i, j)) # print(urls) urlss = [] for i in range(0, 1): urlss.append(urls[i]) # print(urls) # exit() ##############写文件################### # 创建csv文件,用于后面的保存数据 file = open('lianjia.csv', 'w', encoding='utf-8') for url in urls: # 基于for循环,抓取出所有满足条件的标签和属性列表,存放在find_all中 res = requests.get(url) res = res.text.encode(res.encoding).decode('utf-8') soup = BeautifulSoup(res, 'html.parser') find_all = soup.find_all(name='div', attrs={'class': 'info-panel'}) for i in list(range(len(find_all))): # 基于for循环,抓取出所需的各个字段信息 title = find_all[i].find('a')['title'] # 每套二手房的标语 res2 = find_all[i] name = res2.find_all('div', {'class': 'where'})[0].find_all('span')[0].text # 每套二手房的小区名称 room_type = res2.find_all('div', {'class': 'where'})[0].find_all('span')[1].text # 每套二手房的户型 size = res2.find_all('div', {'class': 'where'})[0].find_all('span')[2].text[:-3] # 每套二手房的面积 # 采用列表解析式,删除字符串的首位空格 info = [i.strip() for i in res2.find_all('div', {'class': 'con'})[0].text.split('\n')] region = info[1] # 每套二手房所属的区域 loucheng = info[2][2:] # 每套二手房所在的楼层 chaoxiang = info[5][2:] # 每套二手房的朝向 builtdate = info[-3][2:] # 每套二手房的建筑时间 # 每套二手房的总价 price = find_all[i].find('div', {'class': 'price'}).text.strip()[:-1] # 每套二手房的平方米售价 price_union = find_all[i].find('div', {'class': 'price-pre'}).text.strip()[:-3] # print(name,room_type,size,region,loucheng,chaoxiang,price,price_union,builtdate) # 将上面的各字段信息值写入并保存到csv文件中 file.write(','.join((name, room_type, size, region, loucheng, chaoxiang, price, price_union, builtdate)) + '\n') # 关闭文件(否则数据不会写入到csv文件中) file.close()